Noise Filtering Inverse Transformation

ABSTRACT

A method, system and apparatus for noise filtering inverse transformation (NFIT), recovering phases and amplitudes of singular cycles of data carrying tones or sub-bands from a composite signal such as OFDM, is presented herein. Such NFIT comprises adaptive inverse transformation of non-linear channel transform function and instant accommodation of time variant quickly changing characteristics of transmission channel caused by interferences including line loads, cross-talk or predictable noise.

This application claims priority benefit of U.S. Provisional Application No. 60/894,433 filed on Mar. 12, 2007.

This application claims foreign priority benefit of PCT international application No. PCT/CA07/000885 filed on May 11, 2007 which is incorporated by reference herein as if fully set forth.

This application claims priority benefit of U.S. patent application Ser. No. 11/747,889 filed on May 11, 2007 which is incorporated by reference herein as if fully set forth.

BACKGROUND

1. Field

The present disclosure is directed to noise filters for serial data link receivers including all receivers for copper/optical/wireless links for local and remote data transmissions.

More particularly, this disclosure presents low cost high resolution noise filtering inverse transformation method, systems and apparatus (NFIT) for precise recovery of originally transmitted signals from noisy received signals.

Solutions presented herein comprise systems and methods for programmable noise filtering from over-sampled wave-forms, carrying variable lengths data encoding pulses, which transfer data rates ranging up to ½ of technology's maximum clock frequency.

The NFIT shall be particularly advantageous in system on chip (SOC) implementations of signal processing systems.

2. Background

The purpose of noise filters is to reconstruct original signal by reduction of received signal components representing noise and/or by enhancement of received signal components representing the original signal.

Limitations of conventional noise filtering methods and electronic circuit technologies cause that only linear time invariant filters (LTI filters) can be used in majority of serial communication links.

Such LTI approximations impair filtering efficiency of the majority of the communication links which are non-linear and time variant and have changing in time characteristics.

Furthermore due to such limitations of conventional solutions; even rarely used non-linear and/or adaptive filters using adaptive algorithms to accommodate changing in time characteristics of transmission channels, can accommodate only limited and slowly changing portions of signal non-linearity and/or distortion caused by nonlinear and/or changing in time characteristics of transmission channel.

Frequency sampling filters (FSF) capable of recovering particular sinusoidal tones/sub-bands from a composite signal such as OFDM frame, were known and described as rarely used in the book by Richard G. Lyons; “Understanding Digital Signal Processing”, Second Edition 2004 Prentice Hall.

However such frequency sampling filters and other conventional frequency domain methods did not have time domain solutions needed to preserve and recover phase alignments of singular cycles of tones/sub-bands to the composite signal frame, wherein such phase alignments carry phases of tones/sub-bands transmitting data encoded originally.

It is the objective of solutions presented herein to alleviate such limitations by contributing; accommodation of unlimited non-linearity and time variant quick changes of transmission channel such as those caused by line load, cross-talk and inter-band interference from adjacent transmission channels,

and said time domain solutions enabling combination of frequency domain and time domain signal processing methods allowing recovery of phases and amplitudes of singular cycles of data carrying tones or sub-bands comprised in the composite signal.

SUMMARY

The present disclosure eliminates said fundamental deficiencies of conventional noise filters as it is explained below.

This disclosure comprises a recovery of the originally transmitted signal by reversing functioning of the transmission channel which distorts and introduces interferences to the transmitted signal, wherein such reversal is accomplished by applying an inverse transformation to a received signal distorted by the transmission channel additionally introducing transmission link noise and adjacent channels/bands interferences.

Since every transmission channel performs some kind of transformation of a transmitted signals space consisting of originally transmitted shapes into a received signal space substantially different due to transmission channel distortion and interferences, such inverse transformation can provide effective universal means for noise filtering recovery of original signal.

Consequently this disclosure includes a method, a system and an apparatus for noise filtering inverse transformation of a received signal (NFIT) which eliminates non-linear and other distortions and interferences of the transmission channel from the received signal, by comprising:

capturing an over-sampled received signal waveform;

pre-filtering and/or analyzing such captured waveform in order to detect which predefined set of waveform shapes includes the analyzed waveform, wherein such predefined set of shapes is designed as implementing a known transformation of an originally transmitted signal;

a recovery of said originally transmitted signal (freed of transmitting channel distortions and interferences) by applying the inverse transformation of said known transformation of the original signal into the predefined sets of shapes;

wherein such original signal transformation and its inverse transformation can be derived as theoretical models and/or results of training session and/or results of adaptive optimization process.

The NFIT further comprises comparing such captured waveform shape with a frame of reference (named also as mask) characterizing such set of predefined shapes in order to verify which set of predefined shapes the captured shape shall belong to, wherein:

an captured waveform interval is compared with such mask by producing an estimate of their shapes similarity (named also as proximity estimate), such as correlation integral or deviation integral between samples belonging to the waveform and their counterparts belonging to the mask;

such estimate of shapes similarity (proximity estimate) is used to detect, if the set of shapes characterized by the mask used corresponds to the captured waveform;

the inverse transformation of the known transmission channel transformation is applied to the mask characterizing such corresponding set of shapes, in order to recover the original signal free of transmission channel distortions and interferences.

Such NFIT further includes instant accommodation of time variant quickly changing characteristics of transmission channel, caused by interferences such as line loads or cross-talk or inter-band interference; by comprising:

producing real time evaluations of such instantly changing interferences;

using such real time evaluations for selection of a different frame of reference accommodating such instant interferences;

using such selected different frame for said detection of set of shapes corresponding to the captured waveform affected by the instant interferences;

said recovery of the original signal by performing the inverse transformation of the frame of reference characterizing such detected set of shapes.

The NFIT includes a method for data recovery from received DMT or Multi-band frames wherein such data recovery method comprises integration of frequency domain and time domain signal processing methods, wherein:

a DMT or Multi-band signal is filtered in frequency domain wherein frequency filters used produce filtered signals maintaining known phase relation to said DMT or Multi-band frame (signals are filtered in phase with the frame);

said time domain time signal processing of such frequency filtered signals performed in phase with the frame measures amplitudes and/or phases of singular half-cycles or cycles of DMT or Multi-band tones;

such measured amplitudes and/or phases are inversely transformed by reversing their distortions introduced by a transmission channel, of said DMT or Multi-band composite signal, including distortions of previous processing stages;

such inversely transformed amplitudes and phases are used to recover data symbols originally encoded in the transmitted signal;

every set of data symbols recovered from amplitudes and/or phases of a particular tone or band-frequency, is processed using statistical methods in order to select the most probable symbol.

Such data recovery method includes sensing amplitudes and/or phases of signals surrounding said data carrying tone or band signal and reversing distortions of such data signal caused by said surrounding signals, wherein such data recovery method comprises:

detection of amplitudes and/or phases of noise and/or other signals surrounding such particular data carrying signal in frequency domain and/or in time domain;

using such detected amplitudes and/or phases of noise and/or other surrounding signals for deriving estimates of data signal distortions introduced by the surrounding noise and/or other signals; using such distortions estimates for performing reverse transformation of said detected amplitudes and/or phases of the data carrying signal into the amplitudes and/or phases corresponding to the data signal transmitted originally.

This disclosure further includes using a synchronous circular processing (SCP) method and apparatus for very fast front-end signal processing performing real time functions, such as:

continuous waveform over-sampling and capturing, said instant interferences evaluations, waveform interval analysis and comparison with pre-selected masks and other signal processing in time domain and/or frequency domain.

The SCP comprises sequentially connected stages fed with digital samples produced by A/D converter of incoming wave-form; wherein:

such sequential stage comprises a register for storing and/or processing sequentially multiple wave-form samples;

such sequential stage register comprises circularly used segments designated for storing and/or processing of consecutive samples assigned to consecutive segments of the stage by an index circulating within a stage segments number;

such storing and/or processing of consecutive samples in the segments of the sequential stage is driven by consecutive circular clocks designated for the particular stage wherein every such consecutive circular clock is applied to its designated segment at a time instant occurring periodically within a circulation cycle.

outputs of a segment or segments of this sequential stage register can be used by a next sequential stage while other segment or segments of this stage are still accepting and/or processing other samples during this stage's circular cycle.

Such SCP enables configuration comprising variety of said consecutive stages having widely varying sizes of registers defined by the stages segments numbers adjusted to accommodate data processing requirements between consecutive stages.

Consequently; a number of said stage segments can be adjusted freely to accommodate FIR and/or IIR filters having different orders, and any required extension of processing time can be achieved by adding a corresponding sampling intervals number by increasing number of segments in the stage register.

The SCP further comprises;

using such SCP stage for implementing digital FIR or IIR filter producing output which maintains a known phase displacement towards (is in phase with) a composite input signal;

using such SCP stages for implementing time domain filters producing outputs which are in phase with the composite input signal;

and combining such stages implementing such FIR and/or IIR filters and/or such time domain filters, into a signal processing system producing multiple outputs which are in phase with the composite input signal.

This disclosure still further includes using a programmable control unit (PCU) for on-line back-up processing providing very comprehensive and versatile programmable functions such as:

controlling operations of received waveform screening and capturing circuit (WFSC) providing received waveform samples needed for said received waveform analysis which uses a training session and/or implements an adaptive noise filtering procedure updating inverse transfer function while usual received signal filtering is still taking place uninterrupted;

calculating and implementing the inverse transformation function based on received waveform analysis;

pre-loading said masks used by the SCP;

controlling all said SCP operations by pre-loading SCP control registers which define functions performed by SCP.

The NFIT further includes inverse transformation of band filtered signal (ITBFS) method, system and apparatus for recovering the original signal from a received signal pre-filtered by a band-pass filter wherein the inverse transformation is applied to such pre-filtered signal in order to compensate transmission channel changes caused by instant interferences such as inter-band interferences and/or line loads and/or cross-talk; wherein the ITBFS comprises:

using a band-pass filter for producing a pre-filtered received signal from the captured waveform;

producing real time evaluations of such instant interferences which are in-phase with the pre-filtered signal;

using such real time evaluations for selection of a different frame of reference accommodating such instant interferences;

using such selected different frame for said detection of set of shapes corresponding to the pre-filtered signal affected by the instant interferences;

said recovery of the original signal by performing the inverse transformation of the frame of reference characterizing such detected set of shapes.

The ITBFS includes said producing of real time evaluations further comprising:

a time domain processing of the captured waveform and/or other pre-filtered frequency bands adjacent to the pre-filtered signal band, wherein such time domain processing of the waveform and/or the adjacent frequency bands produce results which are in phase with the pre-filtered signal in order to accomplish in-phase application of said selected frame of reference to the pre-filtered signal.

The ITBFS further includes:

using said SCP for implementing a band-pass filter producing a pre-filtered received signal from the captured waveform;

using said SCP for such time domain processing of the captured waveform and/or other pre-filtered frequency bands adjacent to the pre-filtered signal band, in order to produce said results being in phase with the pre-filtered signal.

This disclosure comprises using NFIT and ITBFS for recovering originally transmitted signal in ADSL/VDSL wireline systems; by comprising the steps of:

using said SCP for implementing band-pass filters for pre-filtering of discrete tone signals from the captured waveform;

using said SCP for said time domain processing of the captured waveform and/or such pre-filtered tone signals adjacent to a specific pre-filtered tone signal, wherein such time domain processing of the waveform and/or the adjacent pre-filtered tone signals produce results which are in phase with the specific pre-filtered tone signal in order to accomplish in-phase application of said selected frame of reference to the specific pre-filtered tone signal.

wherein by applying such steps to every specific discrete tone signal, all originally transmitted discrete tone signals are recovered.

Furthermore this disclosure comprises using NFIT and ITBFS for recovering originally transmitted signal in wide variety of other communication systems as well; including wireless communication systems such as Multi-Band systems and WiLAN.

The NFIT further includes inverse transformation of frequency domain representation of received signal (ITFDR) method and system for filtering out noise from a received signal frequency spectrum and for correcting transmission channel transformation by an inverse transformation of the received signal spectrum into an originally transmitted signal; wherein:

such received frequency spectrum is compared with a spectrum mask, by producing a spectrum proximity estimate such as correlation integral or deviation integral between components of the received frequency spectrum and their counterparts from the spectrum mask;

such proximity estimate is used to detect, if the spectrum mask applied represents a noise filtered version of the received frequency spectrum;

said inverse transformation of said transmission channel transformation is applied to the spectrum mask representing such noise filtered received spectrum, in order to recover the originally transmitted signal.

Such ITFDR comprises:

using variety of spectrum masks for approximating different said received spectrums and/or for filtering out different predictable and/or random noise components;

using multiple consecutive proximity estimates for identifying such spectrum mask which is most effective in said approximating of the received spectrum and in said noise filtering;

or using proximity estimates providing indication which spectrum mask shall be applied as the next in order to detect such most effective spectrum mask;

wherein the selection of the next applied spectrum mask is determined by results of real-time in-phase processing of the received signal and/or received spectrum and/or by previous proximity estimates.

The ITFDR method and system includes recovering the original signal from a received signal wherein Fast or Discrete Fourier Transform of received signal, in such communication systems as wireless multi-band OFDM (Orthogonal Frequency Division Multiplexing) or wireline ADSL/VDSL DMT, is inversely transformed in order to recover the content of originally transmitted data carrying signal; wherein the ITFDR comprises:

producing real time evaluations of frequency spectrums of major interferences affecting frequency bands or discrete tones reproduced by FFT/DFT, wherein such major interferences include said re-produced by FFT/DFT frequency spectrums influencing themselves and adjacent frequency bands/tones or other significant interferences;

using such real time evaluations for a selection of a spectrum mask most suitable for accommodating such major interference sources when it is applied to a specific band/tone reproduced by FFT/DFT;

wherein a specific band/tone spectrum reproduced by FFT/DFT is compared with such selected spectrum mask, by producing a spectrum proximity estimate such as correlation integral or deviation integral between components of the band/tone spectrum and their counterparts from the spectrum mask;

wherein such proximity estimate is used to detect, if the set of spectrums characterized by the spectrum mask corresponds to the specific band/tone spectrum;

the inverse transformation of the known transmission channel transformation is applied to the spectrum mask characterizing such corresponding set of spectrums, in order to recover the original frequency domain signal free of transmission channel interferences;

wherein by applying such steps to every specific band/tone spectrum, original frequency domain signals transmitted over all bands/tones are recovered.

The ITFDR further comprises:

analyzing frequency spectrums produced by the FFT or DFT during a training session or adaptive control procedure in order to derive said spectrum masks corresponding to specific data symbols transmitted over specific frequency bands or discrete tones, wherein for every specific data symbol variety of different spectrum masks can be derived wherein such different spectrum masks correspond to different content of major sources of interference such as adjacent frequency bands or discrete tones;

wherein such derivation of the spectrum masks includes derivation of an inverse transform of transmission channel transformation which can be used for said recovery of original frequency domain signals based on an analysis of said corresponding to them frequency spectrums reproduced by FFT/DFT on the receiver side.

The NFIT introduced with above exemplifications , further includes methods and systems characterized below.

The NFIT includes inverse transformation of received signal (ITRS) method and system and apparatus, for recovering originally transmitted signal and for filtering out transmission channel transformation and noise by applying an inverse transformation of said received signal into the original signal; wherein:

a representation of said received signal is compared with a mask, by producing an proximity estimate such as correlation integral or deviation integral between components of the received signal representation and their counterparts from the mask;

such proximity estimate is used to detect, if the mask applied represents a noise filtered version of the received signal representation;

said inverse transformation of said transmission channel transformation is applied to the mask representing such noise filtered received signal, in order to recover the originally transmitted signal.

Said ITRS comprises:

using variety of such masks for approximating different representations of said received signal and/or for filtering out different predictable and/or random noise components;

and/or using results of real-time in-phase processing of such representation of received signal or representations of predictable interferences, for such selections of next masks applied;

using multiple consecutive proximity estimates for selecting said mask which is most effective in said approximating of the received signal representation and in said noise filtering;

and/or using a numerical result of a previous proximity estimate for selecting such most effective mask.

Said ITRS further comprises:

using an over-sampled waveform of a time interval of received signal as said received signal representation;

or using a frequency spectrum of said time interval of received signal as said received signal representation.

Said ITRS using the over-sampled interval waveform as signal representation, comprises preliminary characterization of a shape of waveform interval wherein a result of such interval shape characterization facilitates selection of an interval mask applied to the interval waveform in order to find a correct approximation of received signal interval freed of unpredictable noise; wherein such ITRS comprises:

evaluation of averaged peak amplitude of internal pulses occurring within the interval waveform;

evaluation of averaged phase, of periodical edges of such internal interval pulses, versus a known phase reference;

using such averaged amplitude and averaged phase for selecting said interval mask.

The ITRS comprises applying plurality of such masks and analyzing their proximity estimates in order to optimize selection of a final interval mask transformed inversely into the original signal; wherein such ITRS comprises:

applying said interval masks having different amplitudes and analyzing their proximity estimates in order to find an interval mask providing closest amplitude wise approximation of the interval waveform;

and/or applying said interval masks having different phases and analyzing their proximity estimates in order to find an interval mask providing closest phase wise approximation of the interval waveform;

inverse transformation of the interval mask, providing such closest approximation, into the originally transmitted signal.

The ITRS further comprises different application methods of said interval masks having different phases versus said received signal waveform; wherein:

plurality of said interval masks, having different phases versus the same interval waveform, is applied and their proximity estimates are analyzed in order to find said closest phase wise approximation of the interval waveform;

or the same interval mask is applied to plurality of phase shifted interval waveforms and resulting proximity estimates are analyzed, in order to find the optimum phase of the interval mask versus the received signal waveform approximated by that mask.

This disclosure comprises methods, systems and solutions described below.

1. A method for noise filtering inverse transformation (NFIT), comprising a recovery of an original signal by reversing functioning of the transmission channel distorting the original signal, wherein such reversal is accomplished by applying an inverse transformation to a received signal wherein such distortion includes transmission link noise and adjacent channel or band interference; the NFIT method comprising the steps of:

capturing an over-sampled waveform of said received signal;

pre-filtering or analyzing such captured waveform in order to detect which predefined set of waveform shapes includes pre-filtered or analyzed waveform, wherein such predefined set of shapes is designed as implementing a known transformation of the original signal;

the recovery of said original signal by applying the inverse transformation of said known transformation of the original signal into the predefined sets of shapes;

wherein such original signal transformation and its inverse transformation can be derived using theoretical models or results of training session or results of adaptive optimization process.

2. A method for noise filtering inverse transformation (NFIT), comprising a recovery of an original signal by reversing functioning of the transmission channel distorting the original signal, wherein such reversal is accomplished by applying an inverse transformation to a received signal wherein such distortion includes transmission link noise and adjacent channel or band interference; the NFIT method comprising the steps of:

capturing an over-sampled waveform of said received signal;

comparing an interval of the captured waveform with a mask used as frame of reference characterizing

a set of predefined shapes, in order to verify which set of predefined shapes such waveform interval corresponds to,

wherein such comparison includes

producing a proximity estimate, estimating similarity of their shapes, such as correlation integral or deviation integral between samples belonging to the waveform interval and their counterparts belonging to the mask,

and using such proximity estimate for verifying if the set of shapes characterized by the mask used corresponds to the captured waveform shape;

applying the inverse transformation of the known transmission channel transformation to the mask characterizing such corresponding set of shapes, in order to recover the original signal.

3. A method for noise filtering inverse transformation (NFIT), comprising a recovery of an original signal by reversing functioning of the transmission channel distorting the original signal, wherein such reversal, accomplished by applying an inverse transformation to a received signal, includes accommodation of time variant quickly changing characteristics of transmission channel caused by interference including line load or cross-talk or inter-band interference; the NFIT method comprising the steps of:

capturing an over-sampled waveform of a received signal;

producing a real time evaluation of such instantly changing interference;

using such evaluation for pre-selection of a mask used as frame of reference characterizing a set of predefined shapes of said received signal, in order to accommodate such interference; comparing an interval of the captured waveform with such pre-selected mask, in order to verify which set of predefined shapes such waveform interval corresponds to,

wherein such comparison includes producing a proximity estimate, estimating similarity of their shapes, such as correlation integral or deviation integral between samples belonging to the waveform interval and their counterparts belonging to the mask,

said recovery of the original signal by performing the inverse transformation of the frame of reference characterizing such detected set of shapes.

4. A method for data recovery from a composite frame (DRCR) comprising discrete multiple tones

(DMT) or multiple sub-bands (MSB) wherein such data recovery method comprises combination of frequency domain and time domain signal processing methods, the DRCR method comprising the steps of:

frequency domain filtering of a composite signal carrying such composite frame wherein frequency filters produce discrete tones or sub-bands maintaining known phase relation to said DMT or MSB frame i.e. said frequency filters keep their outputs in phase with the composite frame;

said time domain time signal processing of such discrete tones or sub-bands performed in phase with the composite frame, measures amplitudes and/or phases of singular half-cycles or cycles of discrete tones or sub-bands;

such measured amplitudes and/or phases are inversely transformed by reversing their distortions introduced by a transmission channel of said composite signal, including distortions caused by previous processing stages;

such inversely transformed amplitudes and phases are used to recover data symbols originally encoded in the transmitted signal.

5. A DRCR as described in clause 4, wherein the DRCR method comprises the step of: selecting the most probable symbol by using statistical methods for processing a set of data symbols recovered from amplitudes and/or phases of a particular tone or sub-band.

6. A method for data recovery from a composite signal (DRCS) comprising discrete multiple tones (DMT) or multiple sub-bands (MSB) wherein a combination of frequency domain and time domain signal processing methods is utilized, wherein amplitudes and/or phases of signals surrounding a particular data carrying tone or sub-band are sensed and distortions of said particular tone or sub-band are reversed; the DRCS method comprising the steps of:

frequency domain filtering of said composite signal producing discrete tones or sub-bands maintaining known phase relation to said DMT or MSB frame;

said time domain signal processing of such discrete tones or sub-bands measures amplitudes and/or phases of singular half-cycles or cycles of discrete tones or sub-bands;

detection of amplitudes and/or phases of noise and/or other signals surrounding such particular tone or sub-band, performed in frequency domain and/or in time domain;

using such detected amplitudes and/or phases for deriving estimates of distortions introduced by the surrounding noise and/or other signals to such particular tone or sub-band; using such distortions estimates for performing reverse transformation of said detected amplitudes and/or phases of such singular half-cycles or cycles of the particular tone or sub-band into the amplitudes and/or phases corresponding to data transmitted originally.

such reversely transformed amplitudes and phases are used to recover data symbols originally encoded in the transmitted signal.

7. A synchronous circular processing (SCP) system for a front-end signal processing performing real time functions including continuous over-sampling and capturing of a received signal waveform, and time domain processing of the captured waveform producing outputs maintaining a known phase displacement towards the received signal i.e. being in phase with it; the SCP system comprising:

sequentially connected stages fed with digital samples produced by an A/D converter of received wave-form;

such sequential stage comprises a register for storing and/or processing sequentially multiple wave-form samples;

such sequential stage register comprises circularly used segments designated for storing and/or processing of consecutive samples assigned to consecutive segments of the stage by an index circulating within a stage segments number;

such storing and/or processing of consecutive samples in the segments of the sequential stage is driven by consecutive circular clocks designated for the particular stage wherein every such consecutive circular clock is applied to its designated segment at a time instant occurring periodically within a circulation cycle;

outputs of a segment or segments of this sequential stage register can be used by a next sequential stage while other segment or segments of said particular stage are still accepting and/or processing other samples during this stage's circular cycle.

8. A synchronous circular processing (SCP) system for a front-end signal processing performing real time functions including continuous over-sampling and capturing of a received signal waveform, and time domain processing of the captured waveform producing outputs maintaining a known phase displacement towards the received signal i.e. being in phase with it; the SCP system comprising:

sequentially connected stages fed with digital samples produced by an A/D converter of received wave-form;

such sequential stage comprises a register for storing and/or processing sequentially multiple wave-form samples;

such sequential stage register comprises circularly used segments designated for storing and/or processing of consecutive samples assigned to consecutive segments of the stage by an index circulating within a stage segments number;

such storing and/or processing of consecutive samples in the segments of the sequential stage is driven by consecutive circular clocks designated for the particular stage wherein every such consecutive circular clock is applied to its designated segment at a time instant occurring periodically within a circulation cycle;

outputs of a segment or segments of this sequential stage register can be used by a next sequential stage while other segment or segments of said particular stage are still accepting and/or processing other samples during this stage's circular cycle;

wherein said consecutive stages have varying sizes of registers defined by the stages segments numbers adjusted to accommodate data processing requirements between consecutive stages;

wherein plurality of said stage segments accommodates FIR and/or IIR filters which may be of different orders, and any required extension of processing time can be achieved by adding a corresponding sampling intervals number by increasing number of segments in the stage register.

9. A synchronous circular processing (SCP) method for a front-end signal processing performing real time functions including continuous over-sampling and capturing of a received signal waveform and time domain processing of the captured waveform producing outputs maintaining a known phase displacement towards the received signal; the SCP method comprising the steps of:

using sequentially connected stages for storing or processing of samples produced by A/D converter of received wave-form;

wherein such sequential stage comprises a register for storing and/or processing multiple sequential waveform samples;

wherein such sequential stage register comprises circularly used segments designated for storing and/or processing of consecutive samples assigned to consecutive segments of the stage by an index circulating within a stage segments number;

driving such storing and/or processing of consecutive samples in the segments of the sequential stage, by applying consecutive circular clocks designated for the particular stage wherein every such consecutive circular clock is applied to its designated segment at a time instant occurring periodically within a circulation cycle;

using outputs of a segment or segments of a particular sequential stage register by a next sequential stage when other segment or segments of said particular stage are still accepting and/or processing other samples during said circular cycle of the particular stage.

10. A synchronous circular processing (SCP) method for signal processing implementing digital FIR and/or IIR filter producing outputs which maintain known phase displacements towards a composite input signal such as discrete multi-tone (DMT) or multi-sub-band (MSB) i.e. said outputs are in phase with the composite signal; the SCP method comprising the steps of:

using sequentially connected stages for storing or processing of samples produced by an A/D converter of received wave-form;

wherein such sequential stage comprises a register for storing and/or processing multiple sequential samples fed circularly into consecutive segments of such register by consecutive circular clocks;

using outputs of a segment or segments of a particular sequential stage register by a next sequential stage when other segment or segments of the particular stage are still accepting and/or processing other samples during a circular cycle of the particular stage;

using such sequential stage for implementing digital FIR or IIR filter producing output which maintains a known phase displacement towards said composite signal;

or using such sequential stage for implementing time domain filters producing outputs which are in phase with the composite signal.

11. A method of adaptive synchronous circular processing (ASCP) combining use of a front end synchronous circular processor (SCP) performing real time processing of a received signal, with a programmable control unit (PCU) providing back-up processing enabling more comprehensive programmable functions; the ASCP method comprising the steps of:

using sequentially connected stages for storing or processing of samples produced by an A/D converter of a received signal wave-form;

wherein such sequential stage comprises a register for storing and/or processing multiple sequential samples fed circularly into consecutive segments of such register by consecutive circular clocks;

using outputs of a segment or segments of a particular sequential stage register by a next sequential stage when other segment or segments of the particular stage are still accepting and/or processing other samples during a circular cycle of said particular stage;

using said PCU for controlling operations of a circuit for received waveform screening and capturing (WFSC),

wherein such WFSC supplies said PCU with received waveform samples needed for a received waveform analysis;

using said PCU for updating inverse transfer function by utilizing a training session and/or implementing an adaptive noise filtering procedure while received signal processing is taking place uninterrupted;

using said PCU for calculating and implementing an inverse transformation function based on said received waveform analysis, wherein such inverse transformation reverses received signal distortions introduced by a transmission channel of the received signal;

wherein said PCU implements such inverse transformation by controlling SCP operations by pre-loading SCP control registers which define functions performed by SCP.

12. A method of inverse transformation of a band filtered signal (ITBFS) for a recovery of an original signal by applying an inverse transformation to a pre-filtered signal, wherein such inverse transformation includes compensation of transmission channel change caused by an instant interference; wherein the ITBFS method comprises the steps of:

using a band-pass filter for producing a pre-filtered signal from a captured waveform of a received signal;

producing real time evaluation of such instant interference which remains in phase with the pre-filtered signal by maintaining known phase alignment to such signal;

using such real time evaluation for selection of frames of reference accommodating such instant interference,

using such selected frames of reference for detecting a set of shapes corresponding to the pre-filtered signal affected by the instant interference,

wherein such detection includes comparison of such frames of reference, characterizing sets of predefined signal shapes, with a shape of said affected signal;

said recovery of the original signal by performing the inverse transformation of the frame of reference which detected such set of shapes corresponding to the signal affected by interference.

13. An ITBFS method as described in clause 12, wherein said band filtered signal is a tone or sub-band and said received signal is a composite OFDM signal transmitted over a wireline or wireless link and said original signal is the original tone or sub-band incorporated into the composite OFDM signal.

14. A method for reversal of non-linearity (RNL) of amplifier gain or signal attenuation by applying a polynomial transformation to a non-linear signal, wherein the RNL method comprises the steps of:

identification of dependency between amplitude of an original signal and said non-linear signal; using such dependency for defining polynomial approximation thresholds and their slope coefficients and their exponents;

calculating an exponential component for every said threshold exceeded by a sample of said non-linear signal,

wherein such exponential component for the maximum exceeded threshold, is calculated by rising a difference, between the non-linear signal sample and the maximum threshold, to a power defined by said exponent for the maximum threshold;

wherein such exponential component for every other exceeded threshold, is calculated by rising a difference, between the next exceeded threshold and such other exceeded threshold, to a power defined by said exponent for the other exceeded threshold;

calculating an approximation component for every such approximation threshold exceeded by said non-linear signal sample, by multiplying such exponential component by its slope coefficient;

addition of such approximation components, calculated for approximation thresholds exceeded by or equal to the non-linear signal sample;

wherein by such addition of the approximation components, calculated for the approximation thresholds exceeded by or equal to the non-linear signal sample, said non-linearity is reversed.

15. A method for inverse normalization (IN) of OFDM tone or sub-band, comprising reversal of frequency dependent distortion of said tone or sub-band, by applying normalizing coefficients adjusted to a frequency of this tone or sub-band in order to equalize amplitude and phase distortions introduced to the tone or sub-band by an OFDM transmission channel and a signal processing applied; such IN method comprises the steps of:

identification of the frequency related amplitude or phase distortion of such tone or sub-band by sampling and analyzing of a waveform performed by a programmable control unit (PCU);

calculating such normalizing coefficients for the tone or sub-band, performed by said PCU;

using such normalizing coefficients for equalizing such frequency related distortions, performed by a real-time processing unit (RTP).

16. A method for noise compensation (NC) for a data carrying tone or sub-band of OFDM signal, utilizing evaluation of a noise pattern occurring around this tone or sub-band; wherein such NC method comprises the steps of:

detecting noise pattern occurring in frequency domain by using frequency domain processing such as

Frequency Sampling Filter (FSF) for noise sensing in a frequency spectrum incorporating this tone or sub-band;

detecting noise pattern occurring in time domain by using time domain processing for noise sensing over a time intervals including this tone or sub-band;

using a programmable control unit (PCU)

for analyzing such noise pattern in frequency domain or in time domain, in order to create a deterministic or random noise model for such frequency domain or time domain noise pattern,

and for deriving a noise compensation coefficient by utilizing such deterministic or random model;

using such noise compensation coefficient by a Real Time Processor (RTP) for improving signal to noise ratio in the data carrying tone or sub-band;

using the RTP for time domain recovery of data symbols from singular sinusoidal cycles of the tone or sub-band,

applying such noise model for estimating probability of symbols recovered or for dismissing symbols accompanied by high noise levels close in time;

using such probability estimates or dismissals of unreliable symbols for applying statistical methods for a final recovery of an original data symbol, transmitted by the tone or sub-band, from such plurality of data symbols recovered from the singular sinusoidal cycles.

17. A method for time domain data recovery (TDDR) from a tone or sub-band of OFDM composite frame comprising recovery of data symbols from singular sinusoidal cycles or half-cycles of the tone or sub-band; the TDDR method comprising the steps of:

measuring an amplitude of aid singular half-cycle or cycle by calculating an integral of amplitude over a half-cycle or cycle time period;

measuring a phase of the half-cycle or cycle;

comparing said amplitude measure to predefined amplitude thresholds, in order to decode an amplitude related factor;

comparing said phase measure to predefined phase thresholds, in order to decode a phase related factor;

using such amplitude and/or phase related factor for producing an address to a content addressed memory (CAM) storing a counter of half-cycles or cycles detecting occurrences of a particular data symbol during said OFDM composite frame;

wherein such CAM can accommodate plurality of such counters of occurrences of data symbols detected within this tone or sub-band during the OFDM frame;

application of statistical methods for selecting data symbol recovered from this tone or sub-band, by utilizing content of such counters of data symbols occurrences within this tone or sub-band.

It is understood that other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein various embodiments of the invention are shown and described by way of illustration. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modification in various other aspects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows Block Diagram of Inverse Transformation Method in order to introduce major sub-systems defined in FIG. 2-FIG. 11.

FIG. 12 defines Timing Clocks driving the sub-systems shown in FIG. 2-FIG. 11, wherein:

FIG. 12A shows time slots assigned for sub-clocks driving consecutive processing stages,

FIG. 12B shows sub-clocks driving consecutive bits of circular processing stages.

The FIG. 2-FIG. 11 are numbered correspondingly to processed data flow.

All interconnect signals between these figures have unique names identifying their sources and destinations explained in the Description of the Preferred Embodiments utilizing the same names. Inputs supplied from different drawings are connected at the top or left side and outputs are generated on the bottom due to the top-down or left-right data flow observed generally.

Clocked circuits like registers or flip-flops are drawn with two times thicker lines than combinatorial circuits like arithmometers or selectors.

FIG. 2 shows sampling of DMT signal and correction of it's non-linearity.

FIG. 3 shows comb filtering of DMT signal.

FIG. 4 shows Resonating IIR Filter for 129 Tone (129T/RIF).

FIG. 4A shows Resonating IIR Filter 129.5 SubTone (129.5ST/RIF).

FIG. 5 shows integration & amplitudes registration for half-cycles of 129 Tone.

FIG. 5A shows integration & amplitudes registration for half-cycles of 129.5 Sub-Tone.

FIG. 6 shows phase capturing and initialization of tone processing for 129 Tone/128.5 Sub-Tone/129.5 Sub-Tone.

FIG. 7 shows retiming & averaging of positive and negative half-cycles for 129 Tone/128.5 Sub-Tone/129.5Sub-Tone.

FIG. 8 shows amplitude & phase normalization for 129 Tone/128.5 Sub-Tone/129.5Sub-Tone.

FIG. 9 shows accessing noise compensation coefficients for 129 Tone.

FIG. 10 shows using these coefficients for compensating expected noise contributions from the 128.5 Sub-Tone & 129.5 Sub-Tone.

FIG. 11 shows Recovery and Registration of 129T Frame Symbols.

DETAILED DESCRIPTION

Efficient low-power processing of high-speed oversampled data is enabled by implementing real-time processing units (RTPs) which use simplified algorithms based on variable coefficients. These RTPs are controlled by a Programmable Control Unit (PCU) which performs a background processing. This background processing includes implementing adaptive non-linear algorithms which analyze received line signal and intermediate processing results, in order to define such coefficients and to download them to content addressed memories such as the Control Register Set for 129 Tone (mentioned further below as 129T CRS occurring in FIG. 4, FIG. 6 and FIG. 7).

These memories are accessed by the RTPs implementing the Inverse Transformation Method (ITM) outlined in FIG. 1. These RTPs can be implemented as it is detailed below for 129 Tone of DMT Frame.

The RTPs include doing basic sorting out of recovered symbols (shown in FIG. 1) based on symbols occurrence frequencies and noise levels in surrounding sub-tones or tones, while the PCU comprises doing further analysis of such sorted-out symbols including use of adaptive statistical methods for finalizing selection of most credible symbols.

The embodiments presented herein are based on the assumption listed below:

-   -   DMT OFDM Frame has frequency 4 kHz.     -   DMT Frame comprises OFDM Tones numbered from 32 to 255 (such         OFDM Tones have frequencies equal to Tone_NR×4 kHz)     -   The sampling clock 0/Clk (see FIG. 2) is kept in phase with the         DMT Frame, and has sampling frequency 4 kHz×255×16=16.32 Mhz.

The NFIT (see FIG. 1) comprises correction of Peak to Average Amplitude Ratio (PAAR), which reverses non-linear line signal distortion caused by gain limitation of line amplification path when composition of tones having different frequencies & phases ascends into extreme amplitude levels.

The PAAR correction is explained below.

For Ys=Modulus(A/D sample), Ylt=Linearity Threshold, Cs=Compensation Slope;

Yc=Corrected Sample Modulus is calculated as Yc=f(Ys) function defined below:

If Ys>Ylt; Yc=Ys+Cs(Ys−Ylt)²

else; Yc=Ys.

Since such correcting function Yc=f(Ys) maintains continuity of the derivative of the resulting corrected curve, such transformation maintains smooth transition between the non-corrected and corrected regions while it reverses non-linearity occurring originally in the corrected region due to the gain limitation.

Detailed implementation of such PAAR correction is shown in FIG. 2, wherein the A/D samples are written into the Stage1 of the Synchronous Circular Processor (SCP) comprising A/D Buffer0/A/D_Buffer1 driven by the circular sub-clocks 1/Clk0/1/Clk1 accordingly (see FIG. 12 explaining circular sub-clocks applications).

Using 2 buffers having separate processing circuits attached enables two times longer processing times for calculating DMT0/DMT1 values with reversed effects of the gain limitation.

The Linearity Threshold (LinThr(D:0)) is subtracted from the amplitude of the attenuated signal sample (i.e. from the Modulus(A/D Buffer(Sign,D:0)) and such subtraction result is squared and added to the amplitude of the attenuated sample, in order to reverse said gain attenuation.

Any non-linearity can be reversed smoothly (i.e. without derivatives discontinuity) with any accuracy desired by applying polynomial transformation:

Y _(reversed) =C _(s0) Y _(s); if . . . Y _(s) ∈ (0,Y _(t1)]

Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1); if . . . Y_(s) ∈ (Y _(t1) ,Y _(t2)]

Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1) +C _(s2)(Y _(s) −Y _(t2))^(e2); if . . . Y _(s) ∈ (Y _(t2) ,Y _(t3)]

Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t2))^(e1) +C _(s2)(Y _(s) −Y _(t2))^(e2) + . . . +C _(sN)(Y _(S) =Y _(tN))^(eN); if . . . Y _(s) ∈ (Y _(t(N−1)) ,Y _(tN)]

wherein; C_(s0), C_(s1), . . . C_(sN) represent slopes of approximations added at 0, Y_(t1), Y_(t2), . . . Y_(tN) non-linearity thresholds.

The implementation and equations shown above illustrate a method for reversal of gain non-linearity and/or signal attenuation, wherein such method comprises:

identification of dependency between processed signal attenuation and attenuated signal amplitude;

defining approximation thresholds and their approximation slopes and approximation exponents;

calculating an exponential component for every said approximation threshold exceeded by an attenuated signal sample, by rising a difference, between the attenuated sample and its approximation threshold, to a power defined by its approximation exponent;

calculating an approximation component for every such approximation threshold exceeded by an attenuated signal sample, by multiplying such exponential component by its slope coefficient;

addition of such approximation component, calculated for the particular approximation threshold, to the approximation result comprising previous approximation components calculated for previous approximation thresholds exceeded by the attenuated signal sample;

wherein by such addition of the approximation components calculated for the approximation thresholds exceeded by the distorted and/or attenuated signal sample, said gain-non-linearity and/or signal attenuation is reversed.

This disclosure includes implementation of a Finite Impulse Response (FIR) filter with a circularly driven register (i.e. consecutive processed samples are clocked in circularly into the register) connected to circuits processing properly delayed samples supplied by the register. Such register based FIR filter is shown in FIG. 3 wherein the FIR filter is exemplified as the 1−z⁻⁵¹¹ Comb Filter.

The comb filtering based on “1−z⁻⁵¹¹” begins when N+1=512 samples initializing new tone are collected in CFR2(S0:S511), wherein:

the first filtered sample S(511) is filtered with the collected already samples S(0)/S(509) delayed 511/2 times accordingly, in order to produce the output CFS0(Sign,E:0) fulfilling the difference equation v(n)=x(n)−r^(N)x(n−N)−r²x(n−2);

and similarly the second filtered sample S(0) is filtered with the collected already samples S(1)/S(510) delayed 511/2 times accordingly, in order to produce the output CFSE(Sign,E:0) fulfilling the same difference equation.

Said corrected DMT0/DMT1 outputs of the 1st SCP stage are connected to the Comb Filter Register 2 driven by 512 circular clocks 2/Clk0, 2/Clk3, . . . 2Clk511 in order to enable the 1−z⁻⁵¹¹ Comb Filter of 512^(th) order implemented by the 2^(nd) SCP stage.

Such comb filter has 511 zeros assigning 511 Sub-Bands which can be produced by Frequency

Sampling Filters constructed by connecting the output of such Comb Filter to 511 resonating filters defined by the equations:

1/(1−e ^(j2πk/511) z ⁻¹) for k=0, 1, 2, . . . 510.

Such idea is implemented in more practical way in FIG. 3 where all the details are shown and described (such Frequency Sampling Filtering named as Type IV FSF is explained comprehensively in Ref. 2/page 311).

Consequently “even zeros” from the range of ˜64 to ˜510 correspond to even Sub-Bands 64-510 which are considered as facilitating DMT tones numbered from 32T to 255T, while “odd zeros” correspond to separating them odd Sub-Bands numbered from 63-511 which are considered as facilitating noise sensing Sub-Tones numbered as 31.5ST, 32.5ST, and 33.5ST to 255.5ST.

Such naming convention of the Tones and Sub-Tones is used further on in this Section text and drawings.

The Comb Filter shown in FIG. 3 uses selection circuits, connected to the circularly driven Comb Filter

Register 2 (CFR2), for producing consecutive filtered signal samples.

Another possible implementation can use a shifted CFR2 wherein the DMT0/DMT1 signals are clocked into the same segment S0 of the CFR2 and always the same segments S0/S511 can be used, as providing 511 times delay, for producing Comb Filter Output signal.

This disclosure comprises both; the FIR filter, with the circularly driven filter register, using the selection circuits connected to the register for supplying consecutive signal samples, and the FIR filter, with the shifted filter register, utilizing shifting of the filter register for supplying consecutive signal samples.

This disclosure includes implementation of an Infinite Impulse Response (IIR) filter with a circularly driven filter register (i.e consecutive filtered samples are clocked circularly into the register) supplying IIR processing circuits with properly delayed samples. Such IIR filter achieves infinite response characteristic by connecting outputs of such IIR processing circuits back to the inputs of the circularly driven register.

Said IIR Filter with circularly driven register (see FIG. 4), uses selection circuits, connected to the outputs of the Resonator Filter Register (129RFR(S0:S3)), for supplying filter processing circuits which produce consecutive filtered signal samples written back circularly into consecutive samples S0-S3 of 129RFR (S0:S3).

Such circularly driven IIR filter exemplified in FIG. 4, is a resonating filter, having idealistic transfer function (F(z)=1/(1−e^(j2π258/511)z⁻¹)) adjusted into Type IV FSF (explained comprehensively in Ref 2/page 311) for better stability and performance.

Another possible implementation can use a shifted Resonator Filter Register (RFR(S0:S3)) wherein the input signal from the previous stage and outputs of the Resonator Filter Register supply filter processing circuits which produce filtered sample clocked into the same segment S0 of the RFR(S0:S3).

This disclosure comprises both; the IIR filter, with the circularly driven register, using the selection circuits connected to the register outputs for supplying consecutive processed samples, and the IIR filter, with the shifted register, using shifted register outputs for supplying consecutive processed samples to the filter processing circuits.

The odd/even output of the comb filter CFS0(Sign,E:0)/CFSE(Sign,E:0) re-timed in the Comb Filter Reg.3 (CFR3) produces Resonant Filters Selected Input (RFSI(S,E:0)) which is connected to multiple resonating Infinite Impulse Response (IIR) Filters designated for specific Tones or Sub-Tones.

Such resonating IIR filter designated for the 129Tone (129T) is shown in FIG. 4, wherein:

the reference “(from 129T CRS)” indicates that any following constant is provided by its register (belonging to the Control Register Set for 129 Tone), wherein this register is loaded by PCU in order to control operations of Real Time Processor for 129 Tone (129T_RTP);

the coefficient k equals to 2×129=258 for the 129 Tone;

the resonating IIR filtering begins after the CFR3(S0) is produced after collecting N+1=512 samples in CFR2(S0:S511);

the Resonator Filter Register is reset by the signal RESET_RFR(S0:S3) before any new tone IIR filtering begins,

and furthermore such IIR filtering of an entire sequence of N+1=512 samples is completed before using resulting RFR outputs for any further signal processing.

Similar resonating IIR filter designated for the 129.5Sub-Tone (129.5ST) is shown in FIG.4A, wherein:

the reference “(from 129.5ST CRS)” indicates that any following constant is provided by its register (belonging to the Control Register Set for 129.5ST), wherein this register is loaded by PCU in order to control operations of Real Time Processor for 129.5Sub-Tone (129.5ST_RTP);

the coefficient k equals to 2×129.5=259 for the 129.5Sub-Tone;

the resonating IIR filtering begins after the CFR3(S0) is produced after collecting N+1=512 samples in CFR2(S0:S511);

the Resonator Filter Register is reset by the signal RESET RFR(S0:S3) before any new tone IIR filtering begins,

and furthermore such IIR filtering of an entire sequence of N+1=512 samples is completed before using resulting RFR outputs for any further signal processing.

This disclosure comprises implementation of integrating and/or averaging time domain filter with a circularly driven register (i.e consecutive processed samples are clocked in circularly into the register) supplying such filter's integrating/summating circuits with a proper set of integrated/summated samples.

Such time domain filter achieves integration/summation over a consecutive set containing a required number of samples, by circular replacing of the first sample of a previous set, stored in the circular register, with a new sample following the last sample of the previous set. Resulting consecutive set of samples on the circular register outputs is supplied to the filter integrating/summating circuit producing filter output.

Such time domain filter is exemplified in FIG. 5 where it is used for integration of 129T Half-Cycles and for detecting phases of such Half-Cycles (HC) ends, wherein an end of the present HC occurs at the beginning of the next opposite HC.

Since the input to such HC integrating filter has already been filtered by the previous stages FSF, such input must have sinusoidal shape. Therefore resulting integral of amplitudes of 129T HC represents filtered indicator of original amplitude of the 129T sinusoid. Such integral is used for the recovery of the original tone amplitude as it explained later on.

Since such time domain filter and all the previous filters belong to the SCP operating in phase with the

Tones Frame (DMT Frame), such detected HC phase is used for recovering phase of the originally transmitted HC of 129T.

The outputs of the 129T Resonator Filter Register (129RFR(S0:S3)) are clocked in circularly into the Stage5/129 Half Cycle Register (129HCR(S0:S15)) which comprises 16 samples covering an approximated HC interval.

Outputs of 129HCR are connected to the summating circuits producing integral of the last 16 samples long sequence (named Next Integer (NI)).

While Next Integral (NI) of amplitudes of HC long interval is calculated and fed to the Integral Register (IR(0:K)); it is also compared with the Previous Integral (PI) kept in Previous Integral Buffer (PIB(0:K)), in order to verify if Half-Cycle end is reached.

Such HC end occurs when NI<PI/NI>PI is detected following positive/negative HC accordingly.

When end of positive/negative HC is detected, integral of amplitudes over positive/negative HC is loaded into 129 Positive Ampl. Reg. ((129PAR((0:K))/129 Negative Ampl. Reg. (129NAR(0:K)) by signal 129Ld_PA/FE/129Ld_NA/RE accordingly.

Signals 129Ld_PA/FE/129Ld_NA/RE are generated by DecCLK/IncCLK strobes, only if IncCTR>5/DecCTR>5 condition is met. The purpose of such preconditioning is prevention of oscillations (such as caused by computational instability at small signal amplitudes), by providing histeresis introduced by Inc.Counter(0:2)/Dec.Counter(0:2) for positive/negative HC accordingly.

Said IncCTR>5/DecCTR>5 conditions are possible only when the multi-tone processing inhibition signal MTP_Inh is de-activated after initial 640 sampling periods of every new DMT frame (see FIG. 5 and FIG. 6).

The 642 Decoder (shown in FIG. 6) decodes such 640 samples delay introduced by waiting until 640+2 sampling intervals are counted by Frame Samples Counter (FSC), wherein the additional 2 intervals account for the 2 sampling intervals occurring between the MTP Inh generation in the SCP stage 4 and its actual application in the SCP stage 6.

In addition to the prevention of IncCTR>5/DecCTR>5 conditions, MTP_Inh signal inhibits any generation of 129Ld_PHC/129Ld_NHC (see FIG. 6), and thus MTP Inh makes sure that no time domain processing takes place before valid signals are supplied by the frequency domain filters.

Shown in FIG. 5A circuit providing “Half Cycles Integration & Amplitudes Registration for 129.5 Tone”, performs the same operations as the circuit shown in FIG. 5 described above.

Since SCP operations are driven by clocks and sub-clocks maintaining known phase and frequency relation to DMT Frame, results produced by SCP stages maintain known phase relations to DMT Frame as well. Therefore such detection of an end of positive/negative HC can be used to detect phase of Tone cycle producing such HC.

As such detection of positive/negative HC end signals detection of falling/rising edge of 129 Tone sinusoid as well, signal 129Ld_PA/FE/129Ld_NA/RE is used in FIG. 6 for capturing phase of such falling/rising edge by capturing 129 Tone Phase in 129 Falling Edge Reg. (129FER(0:5))/129 Rising Edge Reg. (129RER(0:5) accordingly.

This FIG. 6 shows Phase Capturing and Tone Processing Initialization for the 129T/128.5ST/129.5ST, wherein:

the reference “(from 129T CRS)” indicates that any following constant is provided by its register (belonging to the Control Register Set for 129 Tone), wherein this register is loaded by PCU in order to control operations of Real Time Processor for 129 Tone (129T_RTP);

since 129RisingEdgeReg./129FallingEdgeReg. captures the end of a negative/positive half-cycle, it represents phase of the rising/falling edge accordingly of a sinusoid represented by such negative/positive half-cycle.

Such 129 Tone Phase is produced by subtracting 129 Last Cycle Phase Reg. (such 129LCPR(0:13) specifies nr. of sampling intervals corresponding to the beginning of the presently expected cycle of 129 Tone) from Frame Samples Counter (such FSC(0:12) specifies nr. of sampling intervals which past from the beginning of the present DMT Frame).

Consequently such capture of the 129 Tone Phase defines phase of presently detected cycle of 129 Tone measured in number of sampling intervals which occurred between the beginning of the expected cycle (having 0 phase) and the detected 129T cycle.

Content of 129LCPR is derived by comparing if FSC-LCPR=129 Cycle (129 Cycle represents number of sampling intervals expected during consecutive 129Tone cycle), and by loading FSC into LCPR whenever such equality condition is fulfilled.

In order to avoid accumulation of digitization errors during such multiple comparisons (involving fractional numbers expressing expected lengths of 129Tone cycles); the fractional bit staffing method (defined in Ref. 1) is applied by adding consecutive bits from Fractional Bits Register (FBR(0:128)) to 129CycleBase(0:4). These additions provide consecutive values of 129Cycle(0:5) keeping total digitization error below single sampling interval.

SCP combines in-phase processing in frequency domain with in-phase processing in time domain.

Therefore SCP detects time/phase dependence between noise sub-bands and DMT Tones.

Consequently SCP enables estimating and compensating impact of neighbor noise sub-bands and neighbor tones on specific cycles of particular tones.

Such estimates and compensation use data from training session and from adaptive wave-form sampling and screening for identifying noise patterns and for programming compensation and inverse transformation coefficients by PCU.

Such detection of phase relations is facilitated by capturing falling edge of positive HC of 129T/128.5ST/129.5ST in 129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) by signal 129Ld PA/FE/128.5Ld_PA/FE/129.5Ld_PA/FE.

Similarly rising edge of negative HC of 129T/128.5ST/129.5ST is captured in 129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by signal 129Ld_NA/RE/128.5Ld_NA/RE/129.5Ld_NA/RE.

In order avoid using incomplete HC detected at a beginning of DMT Frame, second appearance of signal LD_PA/FE/LD_NA/RE is required in order to produce signal 129Ld_PHC/129Ld_NHC enabling further processing of 129Tone shown in FIG. 7.

This FIG. 7 shows Retiming & Averaging of Positive and Negative HC for 129T/128.5ST/129.5ST, wherein:

the content of 129FER/128.5FER/129.5FER is processed by the “Modulo-Cycle Adder of Half-Cycle” converting phase of falling edge into a corresponding phase of rising edge,

wherein this phase of rising edge represents phase of sinusoid defined by positive half-cycle ending at the time instant captured in 129FER/128.5FER/129.5FER.

The 7/Clk shown in FIG. 7 generates single PHC/Clk/CYC/Clk impulse if it detects Ld_PHC/Ld_NHC timed by 6/Clk. Such PHC/Clk re-times 129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by re-loading them into 129REB(0:5)/128.5REB(0:5)/129.5REB(0:5), which are:

averaged with 129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) converted into cycle edge by Modulo-Cycle Addition of Half-Cycle);

and re-timed with CYC/Clk loading them into 129AER(0:5)/128.5AER(0:5)/129.5AER(0:5).

The positive amplitude registers 129PAR(0:K)/128.5PAR(0:K)/129.5PAR(0:K) are averaged with 129NAR(0:K)/128.5NAR(0:K)/129.5NAR(0:K) accordingly and loaded into averaged amplitude registers 129AAR(0:K+1)/128.5AAR(0:K+1)/129.5PAR(0:K+1).

SCP comprises using every positive or negative HC as separate data used for recovering tone symbol. Such ability of using singular Half-Cycles for data recovery provides huge data redundancy which facilitates use of statistical methods much more reliable than conventionally used DFT averaging over DMT Frame.

Nevertheless, in order to illustrate implementation having lower power dissipation; SCP exemplified by this embodiment has 7th stage (see FIG. 7) combining amplitudes and phases of positive and negative HC into averages per cycle (which still provide significant redundancy).

The NFIT comprises inversion of frequency related distortions in transmission channel (such as DMT link), by applying different normalizing coefficients to different Carrier Frequencies (such as DMT Tones) wherein such normalizing coefficients are adjusted to equalize amplitude and phase distortions of the transmitted Carrier Freq. including distortions introduced by a signal processing applied; such inverse normalization of amplitudes and phases comprises:

identification of the frequency related distortions occurring on the Carrier Frequencies (or DMT Tones) by using training sessions or adaptive wave-form sampling/screening controlled by PCU;

calculating normalizing coefficients, for such Carrier Frequencies or DMT Tones, by PCU;

using such normalizing coefficients, supplied by PCU, by real-time processing unit for equalizing such frequency related distortions in the processed Carrier Freq. or DMT Tones.

Such amplitude and phase normalization for 129T/128.5ST/129.5ST is shown in FIG. 8, wherein it includes normalization of noise sensing Sub-Tones (128.5ST/129.5ST) neighbor to the data carrying 129T.

129 Tone phase defined by 129 Tone Averaged Edge Register (129AER(0:5)), is normalized by multiplying by the 129T Phase Normalizing coefficient (129Phallor) and by adding the 129T Phase Adjusting coefficient (129PhaAdj).

Since sinusoidal noise contribution from such neighbor sub-tones is dependent on phase differences between the tone and the sub-tones, such phase differences are normalized by multiplying them by the Phase Normalizing coefficient.

129T Averaged Amplitude Register (129AAR(0:K+1)) and its 128.5ST/129.5ST counterparts ((128.5AAR(0:K+1)/(129.5AAR(0:K+1)) are normalized by multiplying them by the 129T Amplitude Normalizing coefficient (129AmpNor).

All such normalizing coefficients are taken from the 129 Tone Control Register Set (129T CRS) which is pre-loaded by PCU implementing adaptive distortion reversing techniques.

While SCP comprises performing signal processing operations which are synchronized by the processed incoming signal, such approach comprises two different synchronization methods specified below and exemplified by the embodiments shown herein.

When SCP stages (such as previous 7 stages) perform processing of belonging to frequency domain

DMT Tones (or Multi-Band carriers); they are synchronized by DMT Frame (or channel frame), as such stages are driven by the clocks or sub-clocks synchronous to the sampling clock which is phase locked to DMT Frame (or channel frame).

When SCP stages (such as this 8^(th) stage and next stages) perform processing of already detected tone (or band) cycles belonging to time domain; they are synchronized by such cycles detection events instead, as such stages are driven by clocks generated when information about cycle detection is passed from a higher level stage to the next level.

Such second synchronization method does not do (discontinues) any further processing if a new cycle of the tone (or band) is not detected.

SCP comprises both synchronization methods defined above.

The cycle detection signal CYC/Clk enables using leading edge of 8/Clk/8 (having frequency 8 times lower than the sampling clock) for the one time activation of AS1/Clk signal which drives all the registers of the SCP 8^(th) stage presented in FIG. 8.

Such AS1/Clk signal remains active (for about 1 sampling period) until the leading edge of the next 9/Clk signal activates the AS1_RST signal (see next FIG. 9).

Such AS1_RST signal enables using leading edge of the next 8/Clk/8 for the one time activation (for about 1 sampling period) of the signal (see FIG. 9) which initiates reading of amplitude and noise compensation coefficients from Memory of Noise Compensation Coefficients (MNCC).

Such timing enables Address Decoders for Memory of Noise Compensation Coeff. (AD_MNCC) to have processing time extended to 8 sampling intervals in order to use normalized amplitudes and phases provided by the previous 8th stage for decoding Address(0:8)/NS_MNCC before AS2/Read_MNCC is activated.

The NFIT comprises efficient non-linear reversing of transmission channel distortions and non-linear noise compensation in over-sampled signals, by implementing real time processing units (RTPs) using simplified algorithms applying variable coefficients, wherein such RTPs are controlled by back-ground processing PCU which implements adaptive non-linear algorithms by analyzing received line signal and intermediate RTPs processing results and by defining and down-loading such coefficients to content addressed memories accessed by RTPs such a 129 Tone Control Registers Set (129T CRS) or Memory of Noise Compensation Coefficients (MMCC).

Such NFIT noise compensation method comprises RTP operations listed below: frequency domain and/or time domain processing of data carrying signal and/or neighbor tones or frequency bands in order to derive estimates of parameters influencing distortion or noise components in the signal, wherein such parameters may include amplitudes and/or phase of data carrying tone or freq. band and/or surrounding noise or interference from neighbor tones or bands;

converting such parameters into an effective address of said content addressed memory in order to access coefficients providing most accurate compensation of said channel distortion or noise;

applying such coefficients to a sequence of predefined arithmetic and/or logical operations performed on the received signal in order to reverse transmission channel distortions and/or to improve signal to noise ratio.

Such noise compensation method is illustrated in FIG. 9 and FIG. 10 showing stage 9^(th) and 10^(th) of the SCP embodiment.

Said noise affecting parameters supplied by 129T/129.5ST Normalized Amplitude Registers (abbreviated to 129NAR(0:P)/129.5NAR(0:P)) and 129.5ST Averaged Phase Difference Register (abbreviated to 129.5APDR(0:L), are used together with the 129 Tone Amplitude Thresholds/Next Sub-tone Amplitude Thresholds (detailed in FIG. 9) and Next-Sub-tone Phase Difference Thresholds; in order to decode address to the Next Sub-tone MNCC (Address(0:8)/NS_MNCC) which selects reading & loading of coefficients, compensating expected noise contribution from the 129.5ST, to their registers 129.5Ampl. Addition Reg/129.5 Ampl. Multiplication Reg./129.5 Phase Addition Reg./129.5 Phase Multiplication Register.

Very similar circuits and methods (shown in lower part of FIG. 9) addressing the Previous Sub-tone MNCC (Address(0:8)/PS_MNCC) are applied in order to select & load coefficients, compensating expected noise contribution from the 128.5ST, to their registers 128.5Ampl. Addition Reg/128.5 Ampl. Multiplication Reg./128.5 Phase Addition Reg./128.5 Phase Multiplication Register.

These loaded from NS_MNCC and PS_MNCC registers supply coefficients producing estimates of noise compensating components which are added to 129T amplitude and to 129T phase (as shown in FIG. 10), in order to provide compensated amplitude in 129 Compensated Amplitude Register (129CAR(0:P,ERR)) and compensated phase in 129 Compensated Phase Register (129CPR(0:L,ERR)).

Such noise compensating coefficients are derived by PCU based on evaluations of noise patterns occurring in tones frequency region and their contributions to signal noise acquired during training session and supported by adaptive wave-form sampling and screening utilizing wide coverage of almost entire spectrum by Tones and Sub-tones detected with FSFs used.

The NFIT comprises:

detecting noise patterns occurring in frequency domain by using frequency domain processing such as

Frequency Sampling Filters for noise sensing in a wide continues frequency spectrum incorporating data carrying tones or frequency bands;

detecting noise patterns occurring in time domain by using time domain processing for noise sensing over time intervals including tone (or band) reception intervals;

using back-ground PCU for analyzing such detected noise patterns and for creating deterministic and random models of occurring noise patterns;

using such models of noise patterns for deriving noise compensation coefficients used by the Real Time Processors for improving signal to noise ratios in received data carrying signal;

taking advantage of the recovered symbols redundancy (assured by the RTPs time domain processing ability of recovering data symbol from every tone cycle) by applying such noise models for estimating probability of symbols recovered and/or for dismissing symbols accompanied by high noise levels close in time;

using such probability estimates and/or dismissals of unreliable symbols for applying statistical methods which are more reliable than conventional DFT averaging of tone signal received.

Such ability of said symbol dismissal, if detected in a vicinity of high noise, is illustrated in FIG. 10, wherein:

the comparison is made if the sum of 129T Amplitude Noise Components (128.5/129.5Amp.NoiseComp.) exceeds 129 Maximum Ampl. Noise (abbreviated to 129Max.AmpNoise) pre-loaded to 129T-CTRS by PCU as a total limit on both acceptable compensations from 128.5ST and 129.5ST taken together.

if the comparison 128.5ANC+129.5ANC>129MAN, the ERROR bit marking such symbol for dismissal is written to the 129CAR(0:P,E).

Similarly for the 129T Phase Noise Components (128.5/129.5Pha.NoiseComp.): if the comparison 128.5PNC+129.5PNC>129MPN, the ERROR bit marking such symbol for dismissal is written to the 129CAP(0:L,E).

The NFIT comprises a method for recovery of data symbol transmitted by a singular half-cycle/cycle of said DMT or Multiband tone, wherein:

an amplitude measure of said singular half-cycle/cycle, such as integral of amplitude over the half-cycle/cycle time period, and a phase measure of the half-cycle/cycle, are applied to a symbol decoder transforming such combination of amplitude and phase measures into a number representing said recovered data symbol.

Such symbol recovery method further comprises:

comparing said amplitude measure to predefined amplitude thresholds, in order to decode an amplitude related factor in recovered symbol definition;

comparing said phase measure to predefined phase thresholds, in order to decode a phase related factor in recovered symbol definition;

wherein such amplitude and phase comparators produce their parts of a binary address to a content addressed memory storing a counter of half-cycles/cycles detecting said symbol occurrences during said DMT or Multi-band signal frame;

wherein such symbols counters memory (SCM) can accommodate different symbols, detected during said DMT or Multi-band frame, varying during the same frame due to said channel distortions and changing in time noise distribution;

sorting out symbols, carried by singular half-cycles/cycles, detected during said DMT or Multi-band frame, in accordance to their detections numbers and/or distributions;

application of statistical methods for selecting data symbol recovered, from said DMT or Multiband tone, such as selection of symbol having higher detections number in a range outlined with statistical distribution models.

Implementation of such data symbol recovery, is exemplified in FIG. 11.

A DMT control registers set (DMT_CRS) programmed adaptively by PCU, supplies said amplitude thresholds (AT1, AT2, AT3, AT4) and said phase thresholds (PT1, PT2, PT3, PT4) to address decoder for symbols counts memory (AD_SCM).

AD_SCM digitizes compensated amplitude/phase provided by CAR(0:P)/CPR(0:L) by comparing them with said amplitude and phase thresholds, in order to produce address ADR(0:3) equal to binary code of symbol detected.

Such ADR(0:3) is applied (as ADR/SCM) to the symbols counts memory (SCM) when the read-write signal (Rd-Wr/SCM) initializes read-write cycle in 129T symbol counts memory (129SCM).

In response to such Rd-Wr/SCM signal said 129SCM provides a content of a symbol counter (129Symb.Count(0:8)) identified by said ADR/SCM.

129Symb.Count is increased by 1 and is written back to the same symbol location in SCM (as updated counter CNT-UPD(0:8)/SCM), if 129SymAcc=1 (i.e. if both Error Bits CAR(E) and CPR(E) are inactive).

However; 129Symb.Count remains unchanged when it is written back to the same SCM location, if 129SymAcc=0 (i.e. if CAR(E) or CPR(E) is active).

Maximum Count of detections of the same symbol discovered in present 129T, is stored in 129Max.CounterReg. (129MCR(0:8)) which is read by PCU at the end of DMT frame.

Any consecutive updated counter CNT-UPD/SCM (abbreviated as 129SC+1) is compared with such 129Max.CounterReg. (abbreviated as 129MCR).

If (129SC>129MCR)=1; the newly updated counter is loaded to said 129Max.CounterReg., and the address of the newly updated counter (equal to the binary code of the symbol detected) is loaded to 129Max.Cont.Addr.Reg. (129MCAR(0:3) which is read by PCU at the end of DMT frame.

Otherwise if (129SC>129MCR)=0; both 129Max.CounterReg. and 129Max.Cont.Addr.Reg. remain unchanged.

In order to simplify further PCU operations; there is a 129T detected symbols map register (129DSMR(1:16)) which has 16 consecutive bits designated for marking occurrence of the 16 consecutive symbols during DMT frame, wherein particular marking bit is set to 1 if corresponding symbol occurs one or more times. Such 129DSMR(1:16) is read by PCU at the end of DMT frame.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the full scope consistent with the claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more”. All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. 

1. A method for noise filtering inverse transformation (NFIT) comprising a recovery of an original signal, received from a transmission channel which transforms the original signal into a received signal space affected by a transmission channel distortion or interference such as that of a transmission link noise or adjacent channel or frequency band, by reversing this transformation of original signal in order to recover the original signal free of such channel distortion or such interference; the NFIT method comprising the steps of: capturing an over-sampled waveform of said received signal; pre-filtering or analyzing such captured waveform in order to detect which predefined set of waveform shapes corresponds to this pre-filtered or analyzed waveform, wherein such predefined set of shapes is characterized by its frame of reference; the recovery of said original signal by applying an inverse transformation of said original signal transformation to this frame of reference; wherein such original signal transformation and its inverse transformation can be derived using theoretical models or results of training session or results of adaptive optimization process.
 2. A method for noise filtering inverse transformation (NFIT) comprising a recovery of an original signal, received from a transmission channel which transforms the original signal into a received signal space affected by a transmission channel distortion or interference such as that of a transmission link noise or adjacent channel or frequency band, by reversing this transformation of original signal in order to recover the original signal free of such channel distortion or such interference, the NFIT method comprising the steps of: capturing an interval of an over-sampled waveform of said received signal; comparing this captured interval with a mask used as frame of reference characterizing a set of predefined shapes, in order to verify which set of predefined shapes such captured interval corresponds to, wherein such comparison includes producing a proximity estimate such as correlation integral or deviation integral between samples belonging to the captured interval and their counterparts belonging to the mask, and using such proximity estimate for verifying if the set of shapes characterized by the mask used corresponds to a shape of the captured interval; applying the inverse transformation of the known transmission channel transformation to the mask characterizing such corresponding set of shapes, in order to recover the original signal.
 3. (canceled)
 4. A method for data recovery from a composite frame (DRCR) comprising discrete multiple tones (DMT) or multiple sub-bands (MSB) wherein such data recovery method comprises combination of frequency domain and time domain signal processing methods, the DRCR method comprising the steps of: frequency domain filtering of a composite signal carrying such composite frame wherein frequency filters produce discrete tones or sub-bands maintaining known phase relation to said DMT or MSB frame i.e. said frequency filters keep their outputs in phase with the composite frame; said time domain time signal processing of such discrete tones or sub-bands performed in phase with the composite frame, measures amplitudes and/or phases of singular half-cycles or cycles of discrete tones or sub-bands; such measured amplitudes and/or phases are inversely transformed by reversing their distortions introduced by a transmission channel of said composite signal, including distortions caused by previous processing stages; such inversely transformed amplitudes and phases are used to recover data symbols originally encoded in the transmitted signal.
 5. A DRCR as claimed in claim 4, wherein the DRCR method comprises the step of: selecting the most probable symbol by using statistical methods for processing a set of data symbols recovered from amplitudes and/or phases of a particular tone or sub-band.
 6. A method for data recovery from a composite signal (DRCS) comprising discrete multiple tones (DMT) or multiple sub-bands (MSB) wherein a combination of frequency domain and time domain signal processing methods is utilized, wherein amplitudes and/or phases of signals surrounding a particular data carrying tone or sub-band are sensed and distortions of said particular tone or sub-band are reversed; the DRCS method comprising the steps of: frequency domain filtering of said composite signal producing discrete tones or sub-bands maintaining known phase relation to said DMT or MSB frame; said time domain signal processing of such discrete tones or sub-bands measures amplitudes and/or phases of singular half-cycles or cycles of discrete tones or sub-bands; detection of amplitudes and/or phases of noise and/or other signals surrounding such particular tone or sub-band, performed in frequency domain and/or in time domain; using such detected amplitudes and/or phases for deriving estimates of distortions introduced by the surrounding noise and/or other signals to such particular tone or sub-band; using such distortions estimates for performing reverse transformation of said detected amplitudes and/or phases of such singular half-cycles or cycles of the particular tone or sub-band into the amplitudes and/or phases corresponding to data transmitted originally. such reversely transformed amplitudes and phases are used to recover data symbols originally encoded in the transmitted signal.
 7. (canceled)
 8. (canceled)
 9. (canceled)
 10. (canceled)
 11. (canceled)
 12. A method of inverse transformation of a band filtered signal (ITBFS) for a recovery of an original signal by applying an inverse transformation to a pre-filtered signal, wherein such inverse transformation includes compensation of transmission channel change caused by an instant interference; wherein the ITBFS method comprises the steps of: using a band-pass filter for producing a pre-filtered signal from a captured waveform of a received signal; producing real time evaluation of such instant interference which remains in phase with the pre-filtered signal by maintaining known phase alignment to such signal; using such real time evaluation for selection of frames of reference accommodating such instant interference, using such selected frames of reference for detecting a set of shapes corresponding to the pre-filtered signal affected by the instant interference, wherein such detection includes comparison of such frames of reference, characterizing sets of predefined signal shapes, with a shape of said affected signal; said recovery of the original signal by performing the inverse transformation of the frame of reference which detected such set of shapes corresponding to the signal affected by interference.
 13. An ITBFS method as claimed in claim 12, wherein said band filtered signal is a tone or sub-band and said received signal is a composite OFDM signal transmitted over a wireline or wireless link and said original signal is the original tone or sub-band incorporated into the composite OFDM signal.
 14. A method for reversal of non-linearity (RNL) of amplifier gain or signal attenuation by applying a polynomial transformation to a non-linear signal, wherein the RNL method comprises the steps of: identification of dependency between amplitude of an original signal and said non-linear signal; using such dependency for defining polynomial approximation thresholds and their slope coefficients and their exponents; calculating an exponential component for every said threshold exceeded by a sample of said non-linear signal, wherein such exponential component for the maximum exceeded threshold, is calculated by rising a difference, between the non-linear signal sample and the maximum threshold, to a power defined by said exponent for the maximum threshold; wherein such exponential component for every other exceeded threshold, is calculated by rising a difference, between the next exceeded threshold and such other exceeded threshold, to a power defined by said exponent for the other exceeded threshold; calculating an approximation component for every such approximation threshold exceeded by said non-linear signal sample, by multiplying such exponential component by its slope coefficient; addition of such approximation components, calculated for approximation thresholds exceeded by or equal to the non-linear signal sample; wherein by such addition of the approximation components, calculated for the approximation thresholds exceeded by or equal to the non-linear signal sample, said non-linearity is reversed.
 15. A method for inverse normalization (IN) of OFDM tone or sub-band, comprising reversal of frequency dependent distortion of said tone or sub-band, by applying normalizing coefficients adjusted to a frequency of this tone or sub-band in order to equalize amplitude and phase distortions introduced to the tone or sub-band by an OFDM transmission channel and a signal processing applied; such IN method comprises the steps of: identification of the frequency related amplitude or phase distortion of such tone or sub-band by sampling and analyzing of a waveform performed by a programmable control unit (PCU); calculating such normalizing coefficients for the tone or sub-band, performed by said PCU; using such normalizing coefficients for equalizing such frequency related distortions, performed by a real-time processing unit (RTP).
 16. (canceled)
 17. (canceled) 